发表机构
Toyota Research Institute（丰田研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究现实世界冬季驾驶中漂移对安全的作用，提出基于碰撞死亡数据的能漂移的非线性模型预测控制（MPC）系统，经实验与比较，该系统能在多种速度下降低中位车道误差，在危险场景中表现更优。
AI 中文摘要
现实世界中的避撞是研究车辆高侧滑漂移动力学和控制的核心动机，但此类操作的实际益处迄今主要在专门设计的需要漂移的场景中进行测试。在这项工作中，我们探讨了在现实世界冬季驾驶条件下漂移对安全而言是否以及何时可能是最优的问题。我们提出了一种能够漂移的非线性模型预测控制（MPC）系统，该系统基于碰撞死亡数据设计，用于处理实际场景，并在高保真模拟器中针对道路偏离和迎面车辆避撞场景部署该控制器。该控制器能自然地启动并维持漂移操作，当后轴遇到冰块时保持在道路上，并避免迎面车辆滑入其车道。与基准电子稳定控制（ESC）系统的比较表明，能够漂移的控制器如何为了可控性而牺牲稳定性，以精确应对危险的冬季驾驶场景。对随机冰块区域的蒙特卡罗研究进一步表明，在多种速度下，能够漂移的控制器比ESC实现了更低的中位车道误差，同时表明漂移主要出现在较高速度下。
英文摘要
Real-world collision avoidance is a core motivation for studying the dynamics and control of high sideslip drifting in vehicles, yet the practical benefit of such maneuvers has so far primarily been tested in scenarios explicitly engineered to require drifting. In this work, we explore the question of if and when drifting may be optimal for safety in real-world winter driving conditions. We present a drift-capable nonlinear model predictive control (MPC) system designed to handle scenarios grounded in crash fatality data and deploy the controller in a high fidelity simulator across road departure and oncoming vehicle collision avoidance scenarios. The controller naturally initiates and sustains drifting maneuvers to stay on the road when hitting a patch of ice on the rear axle and to avoid an oncoming vehicle that has slid into its lane. Comparisons with a benchmark electronic stability control (ESC) system demonstrate how a drift-capable controller can trade off stability for controllability to precisely maneuver through dangerous winter driving scenarios. A Monte Carlo study over random ice patches further shows that the drift-capable controller achieves lower median lane error than ESC across several speeds, while revealing that drifting emerges predominantly at higher speeds.
Comments8 pages, 8 figures. Accepted at IEEE ITSC 2026
